This paper describes how the performance of AI machines
tends to improve at the same pace that AI researchers get
access to faster hardware. The processing power and memory
capacity necessary to match general intellectual performance
of the human brain are estimated. Based on extrapolation of
past trends and on examination of technologies under
development, it is predicted that the required hardware will
be available in cheap machines in the 2020s.

Brains, Eyes and Machines

Computers have far to go to match human strengths, and
our estimates will depend on analogy and extrapolation.
Fortunately, these are grounded in the first bit of the
journey, now behind us. Thirty years of computer vision
reveals that 1 MIPS can extract simple features from
real-time imagery--tracking a white line or a white spot
on a mottled background. 10 MIPS can follow complex
gray-scale patches--as smart bombs, cruise missiles and
early self-driving vans attest. 100 MIPS can follow
moderately unpredictable features like roads--as recent
long NAVLAB trips demonstrate. 1,000 MIPS will be
adequate for coarse-grained three-dimensional spatial
awareness--illustrated by several mid-resolution
stereoscopic vision programs, including my own. 10,000
MIPS can find three-dimensional objects in
clutter--suggested by several "bin-picking" and
high-resolution stereo-vision demonstrations, which
accomplish the task in an hour or so at 10 MIPS. The data
fades there--research careers are too short, and computer
memories too small, for significantly more elaborate
experiments.

There are considerations other than sheer scale. At 1
MIPS the best results come from finely hand-crafted
programs that distill sensor data with utmost efficiency.
100-MIPS processes weigh their inputs against a wide
range of hypotheses, with many parameters, that learning
programs adjust better than the overburdened programmers.
Learning of all sorts will be increasingly important as
computer power and robot programs grow. This effect is
evident in related areas. At the close of the 1980s, as
widely available computers reached 10 MIPS, good optical
character reading (OCR) programs, able to read most
printed and typewritten text, began to appear. They used
hand-constructed "feature detectors" for parts
of letter shapes, with very little learning. As computer
power passed 100 MIPS, trainable OCR programs appeared
that could learn unusual typestyles from examples, and
the latest and best programs learn their entire data
sets. Handwriting recognizers, used by the Post Office to
sort mail, and in computers, notably Apple's Newton, have
followed a similar path. Speech recognition also fits the
model. Under the direction of Raj Reddy, who began his
research at Stanford in the 1960s, Carnegie Mellon has
led in computer transcription of continuous spoken
speech. In 1992 Reddy's group demonstrated a program
called Sphinx II on a 15-MIPS workstation with 100 MIPS
of specialized signal-processing circuitry. Sphinx II was
able to deal with arbitrary English speakers using a
several-thousand-word vocabulary. The system's word
detectors, encoded in statistical structures known as
Markov tables, were shaped by an automatic learning
process that digested hundreds of hours of spoken
examples from thousands of Carnegie Mellon volunteers
enticed by rewards of pizza and ice cream. Several
practical voice-control and dictation systems are sold
for personal computers today, and some heavy users are
substituting larynx for wrist damage.

More computer power is needed to reach human performance,
but how much? Human and animal brain sizes imply an
answer, if we can relate nerve volume to computation.
Structurally and functionally, one of the best understood
neural assemblies is the retina of the vertebrate eye.
Happily, similar operations have been developed for robot
vision, handing us a rough conversion factor.

The retina is a transparent, paper-thin layer of nerve
tissue at the back of the eyeball on which the eye's lens
projects an image of the world. It is connected by the
optic nerve, a million-fiber cable, to regions deep in
the brain. It is a part of the brain convenient for
study, even in living animals because of its peripheral
location and because its function is straightforward
compared with the brain's other mysteries. A human retina
is less than a centimeter square and a half-millimeter
thick. It has about 100 million neurons, of five distinct
kinds. Light-sensitive cells feed wide spanning horizontal
cells and narrower bipolar cells, which are
interconnected by whose outgoing fibers bundle to form
the optic nerve. Each of the million ganglion-cell axons
carries signals from a amacrine cells, and
finally ganglion cells, particular patch of
image, indicating light intensity differences over space
or time: a million edge and motion detections. Overall,
the retina seems to process about ten one-million-point
images per second.

It takes robot vision programs about 100 computer
instructions to derive single edge or motion detections
from comparable video images. 100 million instructions
are needed to do a million detections, and 1,000 MIPS to
repeat them ten times per second to match the retina.

The 1,500 cubic centimeter human brain is about 100,000
times as large as the retina, suggesting that matching
overall human behavior will take about 100 million MIPS
of computer power. Computer chess bolsters this
yardstick. Deep Blue, the chess machine that bested world
chess champion Garry Kasparov in 1997, used specialized
chips to process chess moves at a the speed equivalent to
a 3 million MIPS universal computer (see Figure 3-4).
This is 1/30 of the estimate for total human performance.
Since it is plausible that Kasparov, probably the best
human player ever, can apply his brainpower to the
strange problems of chess with an efficiency of 1/30,
Deep Blue's near parity with Kasparov's chess skill
supports the retina-based extrapolation.

The most powerful experimental supercomputers in 1998,
composed of thousands or tens of thousands of the fastest
microprocessors and costing tens of millions of dollars,
can do a few million MIPS. They are within striking
distance of being powerful enough to match human
brainpower, but are unlikely to be applied to that end.
Why tie up a rare twenty-million-dollar asset to develop
one ersatz-human, when millions of inexpensive
original-model humans are available? Such machines are
needed for high-value scientific calculations, mostly
physical simulations, having no cheaper substitutes. AI
research must wait for the power to become more
affordable.

If 100 million MIPS could do the job of the human brain's
100 billion neurons, then one neuron is worth about
1/1,000 MIPS, i.e., 1,000 instructions per second. That's
probably not enough to simulate an actual neuron, which
can produce 1,000 finely timed pulses per second. Our
estimate is for very efficient programs that imitate the
aggregate function of thousand-neuron assemblies. Almost
all nervous systems contain subassemblies that big.

The small nervous systems of insects and other
invertebrates seem to be hardwired from birth, each
neuron having its own special predetermined links and
function. The few-hundred-million-bit insect genome is
enough to specify connections of each of their hundred
thousand neurons. Humans, on the other hand, have 100
billion neurons, but only a few billion bits of genome.
The human brain seems to consist largely of regular
structures whose neurons are trimmed away as skills are
learned, like featureless marble blocks chiseled into
individual sculptures. Analogously, robot programs were
precisely hand-coded when they occupied only a few
hundred thousand bytes of memory. Now that they've grown
to tens of millions of bytes, most of their content is
learned from example. But there is a big practical
difference between animal and robot learning. Animals
learn individually, but robot learning can be copied from
one machine to another. For instance, today's text and
speech understanding programs were painstakingly trained
over months or years, but each customer's copy of the
software is "born" fully educated. Decoupling
training from use will allow robots to do more with less.
Big computers at the factory--maybe supercomputers with
1,000 times the power of machines that can reasonably be
placed in a robot--will process large training sets under
careful human supervision, and distill the results into
efficient programs and arrays of settings that are then
copied into myriads of individual robots with more modest
processors.

Programs need memory as well as processing speed to do
their work. The ratio of memory to speed has remained
constant during computing history. The earliest
electronic computers had a few thousand bytes of memory
and could do a few thousand calculations per second.
Medium computers of 1980 had a million bytes of memory
and did a million calculations per second. Supercomputers
in 1990 did a billion calculations per second and had a
billion bytes of memory. The latest, greatest
supercomputers can do a trillion calculations per second
and can have a trillion bytes of memory. Dividing memory
by speed defines a "time constant," roughly how
long it takes the computer to run once through its
memory. One megabyte per MIPS gives one second, a nice
human interval. Machines with less memory for their
speed, typically new models, seem fast, but unnecessarily
limited to small programs. Models with more memory for
their speed, often ones reaching the end of their run,
can handle larger programs, but unpleasantly slowly. For
instance, the original Macintosh was introduced in 1984
with 1/2 MIPS and 1/8 megabyte, and was then considered a
very fast machine. The equally fast "fat Mac"
with 1/2 megabyte ran larger programs at tolerable speed,
but the 1 megabyte "Mac plus" verged on slow.
The four megabyte "Mac classic," the last 1/2
MIPS machine in the line, was intolerably slow, and was
soon supplanted by ten-times-faster processors in the
same enclosure. Customers maintain the ratio by asking
"would the next dollar be better spent on more speed
or more memory?"

The best evidence about nervous system memory puts most
of it in the synapses connecting the neurons. Molecular
adjustments allow synapses to be in a number of
distinguishable states, lets say one byte's worth. Then
the 100-trillion-synapse brain would hold the equivalent
100 million megabytes. This agrees with our earlier
estimate that it would take 100 million MIPS to mimic the
brain's function. The megabyte/MIPS ratio seems to hold
for nervous systems too! The contingency is the other way
around: computers are configured to interact at human
time scales, and robots interacting with humans seem also
to be best at that ratio. On the other hand, faster
machines, for instance audio and video processors and
controllers of high-performance aircraft, have many MIPS
for each megabyte. Very slow machines, for instance
time-lapse security cameras and automatic data libraries,
store many megabytes for each of their MIPS. Flying
insects seem to be a few times faster than humans, so may
have more MIPS than megabytes. As in animals, cells in
plants signal one other electrochemically and
enzymatically. Some plant cells seem specialized for
communication, though apparently not as extremely as
animal neurons. One day we may find that plants remember
much, but process it slowly (how does a redwood tree
manage to rebuff rapidly evolving pests during a 2,000
year lifespan, when it took mosquitoes only a few decades
to overcome DDT?).

With our conversions, a 100-MIPS robot, for instance
Navlab, has mental power similar to a 100,000-neuron
housefly. The following figure rates various entities.

MIPS and Megabytes. to
mimic their behavior. Note the scale. Entities rated by
the computational power and memory of the smallest
universal computer needed is logarithmic on both axes:
each vertical division represents a thousandfold increase
in processing power, and each horizontal division a
thousandfold increase in memory size. Universal computers
can imitate other entities at their location in the
diagram, but the more specialized entities cannot. A
100-million-MIPS computer may be programmed not only to
think like a human, but also to imitate other
similarly-sized computers. But humans cannot imitate
100-million-MIPS computers--our general-purpose
calculation ability is under a millionth of a MIPS. Deep
Blue's special-purpose chess chips process moves like a
3-million-MIPS computer, but its general-purpose power is
only a thousand MIPS. Most of the non-computer entities
in the diagram can't function in a general-purpose way at
all. Universality is an almost magical property, but it
has costs. A universal machine may use ten or more times
the resources of one specialized for a task. But if the
task should change, as it usually does in research, the
universal machine can be reprogrammed, while the
specialized machine must be replaced.

Extrapolation

By our estimate, today's very biggest supercomputers are
within a factor of a hundred of having the power to mimic a human
mind. Their successors a decade hence will be more than powerful
enough. Yet, it is unlikely that machines costing tens of
millions of dollars will be wasted doing what any human can do,
when they could instead be solving urgent physical and
mathematical problems nothing else can touch. Machines with
human-like performance will make economic sense only when they
cost less than humans, say when their "brains" cost
about $1,000. When will that day arrive?

The expense of computation has fallen rapidly and persistently
for a century. Steady improvements in mechanical and
electromechanical calculators before World War II had increased
the speed of calculation a thousandfold over hand calculation.
The pace quickened with the appearance of electronic computers
during the war--from 1940 to 1980 the amount of computation
available at a given cost increased a millionfold. Vacuum tubes
were replaced by transistors, and transistors by integrated
circuits, whose components became ever smaller and more numerous.
During the 1980s microcomputers reached the consumer market, and
the industry became more diverse and competitive. Powerful,
inexpensive computer workstations replaced the drafting boards of
circuit and computer designers, and an increasing number of
design steps were automated. The time to bring a new generation
of computer to market shrank from two years at the beginning of
the 1980s to less than nine months. The computer and
communication industries grew into the largest on earth.

Computers doubled in capacity every two years after the war, a
pace that became an industry given: companies that wished to grow
sought to exceed it, companies that failed to keep up lost
business. In the 1980s the doubling time contracted to 18 months,
and computer performance in the late 1990s seems to be doubling
every 12 months.

Faster than Exponential Growth in
Computing Power. The number of MIPS in $1000 of
computer from 1900 to the present. Steady improvements in
mechanical and electromechanical calculators before World War II
had increased the speed of calculation a thousandfold over manual
methods from 1900 to 1940. The pace quickened with the appearance
of electronic computers during the war, and 1940 to 1980 saw a
millionfold increase. The pace has been even quicker since then,
a pace which would make humanlike robots possible before the
middle of the next century. The vertical scale is logarithmic,
the major divisions represent thousandfold increases in computer
performance. Exponential growth would show as a straight line,
the upward curve indicates faster than exponential growth, or,
equivalently, an accelerating rate of innovation. The reduced
spread of the data in the 1990s is probably the result of
intensified competition: underperforming machines are more
rapidly squeezed out. The numerical data for this power curve are
presented in the appendix.

At the present rate, computers suitable for humanlike robots will
appear in the 2020s. Can the pace be sustained for another three
decades? The graph shows no sign of abatement. If anything, it
hints that further contractions in time scale are in store. But,
one often encounters thoughtful articles by knowledgeable people
in the semiconductor industry giving detailed reasons why the
decades of phenomenal growth must soon come to an end.

The keynote for advancing computation is miniaturization: smaller
components have less inertia and operate more quickly with less
energy, and more of them can be packed in a given space. First
the moving parts shrunk, from the gears in mechanical
calculators, to small contacts in electromechanical machines, to
bunches of electrons in electronic computers. Next, the switches'
supporting structure underwent a vanishing act, from thumb-sized
vacuum tubes, to fly-sized transistors, to ever-diminishing
flyspecks on integrated circuit chips. Similar to printed
circuits before them, integrated circuits were made by a
photographic process. The desired pattern was projected onto a
silicon chip, and subtle chemistry used to add or remove the
right sorts of matter in the exposed areas.

In the mid-1970s, integrated circuits, age 15, hit a crisis of
adolescence. They then held ten thousand components, just enough
for an entire computer, and their finest details were approaching
3 micrometers in size. Experienced engineers wrote many articles
warning that the end was near. Three micrometers was barely
larger than the wavelength of the light used to sculpt the chip.
The number of impurity atoms defining the tiny components had
grown so small that statistical scatter would soon render most
components out of spec, a problem aggravated by a similar effect
in the diminishing number of signaling electrons. Increasing
electrical gradients across diminishing gaps caused atoms to
creep through the crystal, degrading the circuit. Interactions
between ever-closer wires were about to ruin the signals. Chips
would soon generate too much heat to remove, and require too many
external connections to fit. The smaller memory cells were
suffering radiation-induced forgetfulness.

A look at the computer growth graph shows that the problems were
overcome, with a vengeance. Chip progress not only continued, it
sped up. Shorter-wavelength light was substituted, a more precise
way of implanting impurities was devised, voltages were reduced,
better insulators, shielding designs, more efficient transistor
designs, better heat sinks, denser pin patterns and
non-radioactive packaging materials were found. Where there is
sufficient financial incentive, there is a way. In fact,
solutions had been waiting in research labs for years, barely
noticed by the engineers in the field, who were perfecting
established processes, and worrying in print as those ran out of
steam. As the need became acute, enormous resources were
redirected to draft laboratory possibilities into production
realities.

In the intervening years many problems were met and solved, and
innovations introduced, but now, nearing a mid-life 40, the
anxieties seem again to have crested. In 1996 major articles
appeared in scientific magazines and major national newspapers
worrying that electronics progress might be a decade from ending.
The cost of building new integrated circuit plants was
approaching a prohibitive billion dollars. Feature sizes were
reaching 0.1 micrometers, the wavelength of the sculpting
ultraviolet light. Their transistors, scaled down steadily from
1970s designs, would soon be so small that electrons would
quantum "tunnel" out of them. Wiring was becoming so
dense it would crowd out the components, and slow down and leak
signals. Heat was increasing.

The articles didn't mention that less expensive plants could make
the same integrated circuits, if less cheaply and in smaller
quantities. Scale was necessary because the industry had grown so
large and competitive. Rather than signaling impending doom, it
indicated free-market success, a battle of titans driving down
costs to the users. They also failed to mention new contenders,
waiting on lab benches to step in should the leader fall.

The wave-like nature of matter at very small scales is a problem
for conventional transistors, which depend on the smooth flow of
masses of electrons. But, it is a property exploited by a radical
new class of components known as single-electron transistors and
quantum dots, which work by the interference of electron waves.
These new devices work better as they grow smaller. At the scale
of today's circuits, the interference patterns are so fine that
it takes only a little heat energy to bump electrons from crest
to crest, scrambling their operation. Thus, these circuits have
been demonstrated mostly at a few degrees above absolute zero.
But, as the devices are reduced, the interference patterns widen,
and it takes ever larger energy to disrupt them. Scaled to about
0.01 micrometers, quantum interference switching works at room
temperature. It promises more than a thousand times higher
density than today's circuits, possibly a thousand times the
speed, and much lower power consumption, since it moves a few
electrons across small quantum bumps, rather than pushing them in
large masses through resistive material. In place of much wiring,
quantum interference logic may use chains of switching devices.
It could be manufactured by advanced descendants of today's chip
fabrication machinery (Goldhaber-Gordon et al. 1997). Proposals
abound in the research literature, and the industry has the
resources to perfect the circuits and their manufacture, when the
time comes.

Wilder possibilities are brewing. Switches and memory cells made
of single molecules have been demonstrated, which might enable a
volume to hold a billion times more circuitry than today.
Potentially blowing everything else away are "quantum
computers," in which a whole computer, not just individual
signals, acts in a wavelike manner. Like a conventional computer,
a quantum computer consists of a number of memory cells whose
contents are modified in a sequence of logical transformations.
Unlike a conventional computer, whose memory cells are either 1
or 0, each cell in a quantum computer is started in a quantum
superposition of both 1 and 0. The whole machine is a
superposition of all possible combinations of memory states. As
the computation proceeds, each component of the superposition
individually undergoes the logic operations. It is as if an
exponential number of computers, each starting with a different
pattern in memory, were working on the problem simultaneously.
When the computation is finished, the memory cells are examined,
and an answer emerges from the wavelike interference of all the
possibilities. The trick is to devise the computation so that the
desired answers reinforce, while the others cancel. In the last
several years, quantum algorithms have been devised that factor
numbers and search for encryption keys much faster than any
classical computer. Toy quantum computers, with three or four
"qubits" stored as states of single atoms or photons,
have been demonstrated, but they can do only short computations
before their delicate superpositions are scrambled by outside
interactions. More promising are computers using nuclear magnetic
resonance, as in hospital scanners. There, quantum bits are
encoded as the spins of atomic nuclei, and gently nudged by
external magnetic and radio fields into magnetic interactions
with neighboring nuclei. The heavy nuclei, swaddled in diffuse
orbiting electron clouds, can maintain their quantum coherence
for hours or longer. A quantum computer with a thousand or more
qubits could tackle problems astronomically beyond the reach of
any conceivable classical computer.

Molecular and quantum computers will be important sooner or
later, but humanlike robots are likely to arrive without their
help. Research within semiconductor companies, including working
prototype chips, makes it quite clear that existing techniques
can be nursed along for another decade, to chip features below
0.1 micrometers, memory chips with tens of billions of bits and
multiprocessor chips with over 100,000 MIPS. Towards the end of
that period, the circuitry will probably incorporate a growing
number of quantum interference components. As production
techniques for those tiny components are perfected, they will
begin to take over the chips, and the pace of computer progress
may steepen further. The 100 million MIPS to match human brain
power will then arrive in home computers before 2030.

False Start

It may seem rash to expect fully intelligent machines in a few
decades, when the computers have barely matched insect mentality
in a half-century of development. Indeed, for that reason, many
long-time artificial intelligence researchers scoff at the
suggestion, and offer a few centuries as a more believable
period. But there are very good reasons why things will go much
faster in the next fifty years than they have in the last fifty.

The stupendous growth and competitiveness of the computer
industry is one reason. A less appreciated one is that
intelligent machine research did not make steady progress in its
first fifty years, it marked time for thirty of them! Though
general computer power grew a hundred thousand fold from 1960 to
1990, the computer power available to AI programs barely budged
from 1 MIPS during those three decades.

In the 1950s, the pioneers of AI viewed computers as locomotives
of thought, which might outperform humans in higher mental work
as prodigiously as they outperformed them in arithmetic, if they
were harnessed to the right programs. Success in the endeavor
would bring enormous benefits to national defense, commerce and
government. The promise warranted significant public and private
investment. For instance, there was a large project to develop
machines to automatically translate scientific and other
literature from Russian to English. There were only a few AI
centers, but those had the largest computers of the day,
comparable in cost to today's supercomputers. A common one was
the IBM 704, which provided a good fraction of a MIPS.

By 1960 the unspectacular performance of the first reasoning and
translation programs had taken the bloom off the rose, but the
unexpected launching by the Soviet Union of Sputnik, the first
satellite in 1957, had substituted a paranoia. Artificial
Intelligence may not have delivered on its first promise, but
what if it were to suddenly succeed after all? To avoid another
nasty technological surprise from the enemy, it behooved the US
to support the work, moderately, just in case. Moderation paid
for medium scale machines costing a few million dollars, no
longer supercomputers. In the 1960s that price provided a good
fraction of a MIPS in thrifty machines like Digital Equipment
Corp's innovative PDP-1 and PDP-6.

The field looked even less promising by 1970, and support for
military-related research declined sharply with the end of the
Vietnam war. Artificial Intelligence research was forced to
tighten its belt and beg for unaccustomed small grants and
contracts from science agencies and industry. The major research
centers survived, but became a little shabby as they made do with
aging equipment. For almost the entire decade AI research was
done with PDP-10 computers, that provided just under 1 MIPS.
Because it had contributed to the design, the Stanford AI Lab
received a 1.5 MIPS KL-10 in the late 1970s from Digital, as a
gift.

Funding improved somewhat in the early 1980s, but the number of
research groups had grown, and the amount available for computers
was modest. Many groups purchased Digital's new Vax computers,
costing $100,000 and providing 1 MIPS. By mid-decade, personal
computer workstations had appeared. Individual researchers
reveled in the luxury of having their own computers, avoiding the
delays of time-shared machines. A typical workstation was a
Sun-3, costing about $10,000, and providing about 1 MIPS.

By 1990, entire careers had passed in the frozen winter of 1-MIPS
computers, mainly from necessity, but partly from habit and a
lingering opinion that the early machines really should have been
powerful enough. In 1990, 1 MIPS cost $1,000 in a low-end
personal computer. There was no need to go any lower. Finally
spring thaw has come. Since 1990, the power available to
individual AI and robotics programs has doubled yearly, to 30
MIPS by 1994 and 500 MIPS by 1998. Seeds long ago alleged barren
are suddenly sprouting. Machines read text, recognize speech,
even translate languages. Robots drive cross-country, crawl
across Mars, and trundle down office corridors. In 1996 a
theorem-proving program called EQP running five weeks on a 50
MIPS computer at Argonne National Laboratory found a proof of a
boolean algebra conjecture by Herbert Robbins that had eluded
mathematicians for sixty years. And it is still only spring. Wait
until summer.

The big freeze. From
1960 to 1990 the cost of computers used in AI research declined,
as their numbers dilution absorbed computer-efficiency gains
during the period, and the power available to individual AI
programs remained almost unchanged at 1 MIPS, barely insect
power. AI computer cost bottomed in 1990, and since then power
has doubled yearly, to several hundred MIPS by 1998. The major
visible exception is computer chess (shown by a progression of
knights), whose prestige lured the resources of major computer
companies and the talents of programmers and machine designers.
Exceptions also exist in less public competitions, like petroleum
exploration and intelligence gathering, whose high return on
investment gave them regular access to the largest computers.

The Game's Afoot

A summerlike air already pervades the few applications of
artificial intelligence that retained access to the largest
computers. Some of these, like pattern analysis for satellite
images and other kinds of spying, and in seismic oil exploration,
are closely held secrets. Another, though, basks in the
limelight. The best chess-playing computers are so interesting
they generate millions of dollars of free advertising for the
winners, and consequently have enticed a series of computer
companies to donate time on their best machines and other
resources to the cause. Since 1960 IBM, Control Data, AT&T,
Cray, Intel and now again IBM have been sponsors of computer
chess. The "knights" in the AI power graph show the
effect of this largesse, relative to mainstream AI research. The
top chess programs have competed in tournaments powered by
supercomputers, or specialized machines whose chess power is
comparable. In 1958 IBM had both the first checker program, by
Arthur Samuel, and the first full chess program, by Alex
Bernstein. They ran on an IBM 704, the biggest and last
vacuum-tube computer. The Bernstein program played atrociously,
but Samuel's program, which automatically learned its board
scoring parameters, was able to beat Connecticut checkers
champion Robert Nealey. Since 1994, Chinook, a program written by
Jonathan Schaeffer of the University of Alberta, has consistently
bested the world's human checker champion. But checkers isn't
very glamorous, and this portent received little notice.

By contrast, it was nearly impossible to overlook the epic
battles between world chess champion Garry Kasparov and IBM's
Deep Blue in 1996 and 1997. Deep Blue is a scaled-up version of a
machine called Deep Thought, built by Carnegie Mellon University
students ten years earlier. Deep Thought, in turn, depended on
special-purpose chips, each wired like the Belle chess computer
built by Ken Thompson at AT&T Bell Labs in the 1970s. Belle,
organized like a chessboard, circuitry on the squares, wires
running like chess moves, could evaluate and find all legal moves
from a position in one electronic flash. In 1997 Deep Blue had
256 such chips, orchestrated by a 32 processor
mini-supercomputer. It examined 200 million chess positions a
second. Chess programs, on unaided general-purpose computers,
average about 16,000 instructions per position examined. Deep
Blue, when playing chess (and only then), was thus worth about 3
million MIPS, 1/30 of our estimate for human intelligence.

Deep Blue, in a first for machinekind, won the first game of the
1996 match. But, Kasparov quickly found the machine's weaknesses,
and drew two and won three of the remaining games.

In May 1997 he met an improved version of the machine. That
February, Kasparov had triumphed over a field of grandmasters in
a prestigious tournament in Linares, Spain, reinforcing his
reputation as the best player ever, and boosting his chess rating
past 2800, uncharted territory. He prepared for the computer
match in the intervening months, in part by playing against other
machines. Kasparov won a long first game against Deep Blue, but
lost next day to masterly moves by the machine. Then came three
grueling draws, and a final game, in which a visibly shaken and
angry Kasparov resigned early, with a weak position. It was the
first competition match he had ever lost.

The event was notable for many reasons, but one especially is of
interest here. Several times during both matches, Kasparov
reported signs of mind in the machine. At times in the second
tournament, he worried there might be humans behind the scenes,
feeding Deep Blue strategic insights!

Bobby Fischer, the US chess great of the 1970s, is reputed to
have played each game as if against God, simply making the best
moves. Kasparov, on the other hand, claims to see into opponents'
minds during play, intuiting and exploiting their plans, insights
and oversights. In all other chess computers, he reports a
mechanical predictability stemming from their undiscriminating
but limited lookahead, and absence of long-term strategy. In Deep
Blue, to his consternation, he saw instead an "alien
intelligence."

In this paper-thin slice of mentality, a computer seems to have
not only outperformed the best human, but to have transcended its
machinehood. Who better to judge than Garry Kasparov?
Mathematicians who examined EQP's proof of the Robbins
conjecture, mentioned earlier, report a similar impression of
creativity and intelligence. In both cases, the evidence for an
intelligent mind lies in the machine's performance, not its
makeup.

Now, the team that built Deep Blue claim no
"intelligence" in it, only a large database of opening
and end games, scoring and deepening functions tuned with
consulting grandmasters, and, especially, raw speed that allows
the machine to look ahead an average of fourteen half-moves per
turn. Unlike some earlier, less successful, chess programs, Deep
Blue was not designed to think like a human, to form abstract
strategies or see patterns as it races through the
move/countermove tree as fast as possible.

Deep Blue's creators know its quantitative
superiority over other chess machines intimately, but lack the
chess understanding to share Kasparov's deep appreciation of the
difference in the quality of its play. I think this
dichotomy will show up increasingly in coming years. Engineers
who know the mechanism of advanced robots most intimately will be
the last to admit they have real minds. From the inside, robots
will indisputably be machines, acting according to mechanical
principles, however elaborately layered. Only on the outside,
where they can be appreciated as a whole, will the impression of
intelligence emerge. A human brain, too, does not exhibit the
intelligence under a neurobiologist's microscope that it does
participating in a lively conversation.

Agony to ecstasy. In
forty years, computer chess progressed from the lowest depth to
the highest peak of human chess performance. It took a handful of
good ideas, culled by trial and error from a larger number of
possibilities, an accumulation of previously evaluated game
openings and endings, good adjustment of position scores, and
especially a ten-million-fold increase in the number of
alternative move sequences the machines can explore. Note that
chess machines reached world champion performance as their
(specialized) processing power reached about 1/30 human, by our
brain to computer measure. Since it is plausible that Garry
Kasparov (but hardly anyone else) can apply his brainpower to the
problems of chess with an efficiency of 1/30, the result supports
that retina-based extrapolation. In coming decades, as
general-purpose computer power grows beyond Deep Blue's
specialized strength, machines will begin to match humans in more
common skills.

The Great Flood

Computers are universal machines, their potential extends
uniformly over a boundless expanse of tasks. Human potentials, on
the other hand, are strong in areas long important for survival,
but weak in things far removed. Imagine a "landscape of
human competence," having lowlands with labels like
"arithmetic" and "rote memorization",
foothills like "theorem proving" and "chess
playing," and high mountain peaks labeled
"locomotion," "hand-eye coordination" and
"social interaction." We all live in the solid
mountaintops, but it takes great effort to reach the rest of the
terrain, and only a few of us work each patch.

Advancing computer performance is like water slowly flooding the
landscape. A half century ago it began to drown the lowlands,
driving out human calculators and record clerks, but leaving most
of us dry. Now the flood has reached the foothills, and our
outposts there are contemplating retreat. We feel safe on our
peaks, but, at the present rate, those too will be submerged
within another half century. I propose (Moravec 1998) that we
build Arks as that day nears, and adopt a seafaring life! For
now, though, we must rely on our representatives in the lowlands
to tell us what water is really like.

Our representatives on the foothills of chess and theorem-proving
report signs of intelligence. Why didn't we get similar reports
decades before, from the lowlands, as computers surpassed humans
in arithmetic and rote memorization? Actually, we did, at the
time. Computers that calculated like thousands of mathematicians
were hailed as "giant brains," and inspired the first
generation of AI research. After all, the machines were doing
something beyond any animal, that needed human intelligence,
concentration and years of training. But it is hard to recapture
that magic now. One reason is that computers' demonstrated
stupidity in other areas biases our judgment. Another relates to
our own ineptitude. We do arithmetic or keep records so
painstakingly and externally, that the small mechanical steps in
a long calculation are obvious, while the big picture often
escapes us. Like Deep Blue's builders, we see the process too
much from the inside to appreciate the subtlety that it may have
on the outside. But there is a non-obviousness in snowstorms or
tornadoes that emerge from the repetitive arithmetic of weather
simulations, or in rippling tyrannosaur skin from movie animation
calculations. We rarely call it intelligence, but
"artificial reality" may be an even more profound
concept than artificial intelligence (Moravec 1998).

The mental steps underlying good human chess playing and theorem
proving are complex and hidden, putting a mechanical
interpretation out of reach. Those who can follow the play
naturally describe it instead in mentalistic language, using
terms like strategy, understanding and creativity. When a machine
manages to be simultaneously meaningful and surprising in the
same rich way, it too compels a mentalistic interpretation. Of
course, somewhere behind the scenes, there are programmers who,
in principle, have a mechanical interpretation. But even for
them, that interpretation loses its grip as the working program
fills its memory with details too voluminous for them to grasp.

As the rising flood reaches more populated heights, machines will
begin to do well in areas a greater number can appreciate. The
visceral sense of a thinking presence in machinery will become
increasingly widespread. When the highest peaks are covered,
there will be machines than can interact as intelligently as any
human on any subject. The presence of minds in machines will then
become self-evident.